2021/06/09 by Jiarui Gan, Rupak Majumdar, Gan, Jiarui +5 · 3 citations
Decision Sciences · #Auction Theory and Applications #Computer Science and Game Theory (cs.GT) #Decision-Making and Behavioral Economics #FOS: Computer and information sciences #Game Theory and Applications
paper · pdf · doi:10.48550/arxiv.2106.05137
openalex publication_date 2021/06/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We study a dynamic model of Bayesian persuasion in sequential decision-making settings. An informed principal observes an external parameter of the world and advises an uninformed agent about actions to take over time. The agent takes actions in each time step based on the current state, the principal's advice/signal, and beliefs about the external parameter. The action of the agent updates the state according to a stochastic process. The model arises naturally in many applications, e.g., an app (the principal) can advice the user (the agent) on possible choices between actions based on additional real-time information the app has. We study the problem of designing a signaling strategy from the principal's point of view. We show that the principal has an optimal strategy against a myopic agent, who only optimizes their rewards locally, and the optimal strategy can be computed in polynomial time. In contrast, it is NP-hard to approximate an optimal policy against a far-sighted agent. Further, if the principal has the power to threaten the agent by not providing future signals, then we can efficiently compute a threat-based strategy. This strategy guarantees the principal's payoff as if playing against an agent who is far-sighted but myopic to future signals.